Master Data Management is evolving from a reactive, rule-based function into a strategic, intelligence-driven capability.
Traditional approaches often struggle to keep pace with complex, multi-domain data spread across ERP, CRM, PLM, and SCM systems, leaving organizations vulnerable to errors, duplicates, and incomplete records.
Gartner reports that poor data quality costs organizations an average of $12.9 million per year. AI and Machine Learning are transforming this landscape by enabling autonomous processes such as data enrichment, normalization, deduplication, and anomaly detection.
These capabilities allow organizations to proactively maintain high-quality, consistent, and actionable data across customer, product, and MRO domains.
By shifting MDM from a compliance-driven task to a proactive, AI-enabled process, enterprises can turn previously fragmented and error-prone datasets into a single source of truth, fueling better operational decisions, reducing risk, and unlocking new efficiencies across the business.
USE CASES
Processes within master data management include a wide-range of workflows and processes, AI-centric technologies can be deployed across;
Normalization & Standardization of Data Records
Enrichment of Master Data Records
Deduplication
Integrations Across Master Data Domains
Master Data Governance
In the subsequent sections, we will cover how AI technologies can address some of the most recurring, monotonous and time-consuming challenges within each of these.
Normalization & Standardization of Data Records
In enterprise environments, the same item is often described in many ways across plants, systems, or regions using different abbreviations, naming conventions, units of measure, or even languages.
This creates inconsistencies that disrupt analytics, searchability, retrieval, matching-logic and cross-functional collaboration
Some of the issues due to these challenges include – difficulty in matching datasets in bulk, duplication arising due to different naming conventions and various challenges in implementing a data governance program.
A major challenge in Master Data Management is that critical item attributes like dimensions, pressure ratings, and material grades, are often buried in free-text descriptions or technical documents such as PDFs, datasheets, technical manuals, Bills of Materials (BOMs), or CAD drawings.
This unstructured nature makes it difficult to extract, standardize, or even search for these records effectively. AI and ML, particularly Natural Language Processing (NLP), play a key role here.
Trained NLP models, often using Named Entity Recognition (NER), can parse complex descriptions and automatically identify key attributes.
For example, a product description like “SS316 Flanged Ball Valve, PN40, DN25” would be intelligently broken down into: Material = SS316, Pressure = PN40, Size = DN25, and Type = Ball Valve.
Examples:
In a customer master data system, the address of customer A refers to the state “Texas” as “TX” and another record may refer to the state simply as “Texas”, similar inconsistencies in addresses like “Parkway” being referred to as “Pk way” are quite common.
In a Material data system the dimension in the short description of a spare part may be refer to the size in “Inches” or “In” or simply as ″
Issues like these can lead to Overstocking (in case of material master), duplication in communications (in case of a customer master) and a whole swathe of other issues
Before AI, these challenges were maintained through a library of existing taxonomies which was generally not “holistic”, difficult to maintain & track and required continuous updates and only worked with managed within a structured codebase.
Thanks to AI models that are “context aware” and trained on verifiable, large sets of data; these inconsistencies can now be weeded out without much effort based on the adopted taxonomy.
The AI model simply needs to be fed information on the taxonomy that is adopted and model takes care of standardization requirements.
Pro Tip: Choose a self-learning MDM software that can be trained on-the-go by the “Human-in-the-Loop” who verifies the changes and approves it
This way, the self-learning system can become truly autonomous, limiting the data steward’s role to an approver.
1. Abbreviation Expansion and Terminology Mapping: AI models trained on industry data understand that “Mtr” = “Meter”, “SS304” = “Stainless Steel 304”, “CS Ball Vlv” = “Carbon Steel Ball Valve”, and so on. They map such variants to standardized terminology.
2. Unit Normalization and Conversion: Whether dimensions are written as “10mm”, “0.01 m”, or “3IN X 5 YDS”, AI can convert and unify them into the preferred measurement system (e.g., metric), and separate compound fields into structured attributes like Width and Length.
3. Language-Agnostic Structuring: AI models can interpret non-English descriptions and local formats to ensure global consistency.
Example: Recognizing that “Filtros de aceite, 7-1/16 pulg” in Spanish refers to an “Oil Filter, 7-1/16 inch”, and then extracting and mapping it correctly.
Enrichment of Master Data Records
One of the most niggling issues in managing most master datasets has to do with missing information. This is often a result of absent or poor Master data governance practices in the first place. According to Super AGI,
AI data enrichment can improve data quality by up to 90% and reduce processing time by up to 80%
Missing information is tricky to manage since de-duplication, normalization and multi-domain integrations become tricky if the context of a data record is simply not available in the first place.
In the past enrichment of data records was either done manually by a team of subject matter specialists OR through complex automations that required a data warehouse to be maintained in a structured manner.
Building automations in data enrichment earlier required master data software vendors to build data-partnerships to access supplier catalogues, contact data, employee information etc, depending on the data domain in question.
Employing a team of subject matter specialists is quite an expensive proposition. But moreover, the to solve the challenge of missing data also increases, further causing delays and exacerbating the challenges due to poor master data quality.
Agentic AI Systems solve for this challenge as they are both “context-aware” and capable of executing complex tasks like browsing the web, retrieving data from documents and other unstructured sources and filling-in the gaps in the existing data records.
Moreover, MCP protocols are advancing each day, equipping AI agents with enough information to be able to execute rudimentary as well as complex workflows by leveraging third-party software.
In this manner, purpose-trained AI agents, when equipped with the right resources, can solve for almost all data enrichment challenges autonomously, and at a fraction of the time required.
In the case of scanned documents or engineering drawings files, AI systems use Optical Character Recognition (OCR) combined with pattern recognition to extract tabular data or specifications, even when they appear in images or engineering layouts.
This enables organizations to extract data from technical documents and transform it into clean, structured, and searchable master data records.
Some Applications of AI Agents in Master Data Enrichments
Applications
1. Automated Attribute Parsing from Descriptions
AI models, particularly NLP-based ones trained on industry-specific datasets, can intelligently extract structured data from complex, unstructured product or material descriptions.
Example: Converting “3-11/16″OD ,7-1/16″LG” into structured fields such as Outer Diameter and Length in Material Master records.
2. Semantic Understanding for Field Mapping
AI understands the context of words and abbreviations used across industries (e.g., “LG” = Length, “OD” = Outer Diameter), and maps them to standardized data fields.
Example: Recognizing that “BALDWIN 915” refers to a manufacturer and part number, and assigning them accordingly.
3. Unit Harmonization and Conversion
AI standardizes different formats and representations of measurements into a unified format across the dataset, eliminating inconsistencies.
Example: Converting “3IN X 5 YDS” to standardized metric units and splitting them into separate Width and Length
Missing information such as specifications, manufacturer part numbers, or unit of measure, is a common problem, especially in legacy datasets or third-party imports.
These gaps can hinder procurement, compliance, and analytics workflows.
AI and ML offer intelligent ways to fill in these blanks. Using techniques like similarity-based inference, models scan existing, complete records to suggest likely values for incomplete ones.
For example, if a new item “SKF Bearing 6205” is missing its outer diameter, AI can infer the value (e.g., 52mm) from other identical or similar items already in the database.
In addition, AI can cross-reference internal data with external catalogs or supplier databases to pull enriched details, like dimensions, datasheets, lifecycle data, or part alternates.
Predictive models, such as regression algorithms or decision trees, can also be used to estimate numeric fields like voltage, torque, or pressure ratings when not explicitly mentioned.
This level of enrichment ensures more complete and usable master data, minimizes manual data entry, and supports downstream automation, sourcing, and compliance efforts.
It is quite common, especially in the Customer Master Data Domain, to stumble across instances where a data record is missing and email address, phone number or an address.
With a combination of MCP protocols & AI agents, this missing information can be enriched by retrieving data from public sources like LinkedIn and even subscription-based third-party sources like DnB or ZoomInfo.
A similar approach is now being increasingly applied to spare parts and MRO data, where missing or inconsistent details can severely impact maintenance and supply chain performance.
Spare parts data often suffers from incomplete descriptions, missing technical attributes, or duplicate entries spread across multiple systems. This leads to confusion during part searches, delays in maintenance, and redundant procurement.
AI-driven data enrichment helps address these challenges by making spare parts records more complete, consistent, and actionable. By analyzing equipment records, supplier catalogs, and historical data, AI systems can:
1. Infer missing attributes such as part type, material grade, or operating specifications.
For example, in raw material data for chemical plants, AI can infer missing purity levels, hazard codes, or storage instructions by comparing similar materials in the database.
2. Identify manufacturer and part number patterns for harmonization across vendors.
In procurement catalogs, AI can detect duplicate SKUs that exist under slightly different names or vendor codes, helping avoid redundant orders.
3. Standardize naming conventions for cross-plant consistency.
For example, in product master data, AI can identify inconsistent categorization, e.g., a smartphone listed under “Accessories” instead of “Mobile Devices”, and correct it for consistent reporting and analytics.
As per a Gartner report,
Data quality governance priorities, it’s noted that inconsistencies across silos (lack of consistency, completeness, uniqueness of records) are among the most challenging issues for large enterprises.
4. Recommend interchangeable or alternate parts based on similarity in specifications, usage, or historical consumption data.
For instance, if a record lists only “GSKT, 4BOLT, SS316,” AI can recognize it as a stainless-steel gasket, identify its flange type, and even suggest compatible alternatives in stock or from approved suppliers.
This enriched view of spare parts and product data improves maintenance planning, speeds up procurement, and enables inventory optimization, especially in multi-plant operations where part visibility or product master consistency is often fragmented across sites.
Below is our product video, demonstrating how our agentic solution enriches the data from first and thirs party sources:
Data Deduplication
Gartner reports that poor data quality costs organizations an average of $12.9 million per year.
Once the data records have been structured, normalized, standardized and enriched with the help of AI, data deduplication becomes a breeze.
The deduplication process itself doesn’t really deploy much of AI (except in the case of L2 Duplicates), but the preceding steps are key to ensuring accurate de-duplication across the entire dataset.
Duplicate entries in a master data set are typically categorized into 2 buckets – L1 & L2
L1 de-duplication is simple and straightforward, in this case, the entire dataset is de-duplicated based on a single logic.
Example 1: Same Material ID in case of direct materials.
Example 2: Same email address or phone number in case of a customer master data
Example 3: Same Manufacturer Partner Number (MPN) in case of MRO spare parts data.
Essentially, any data record with the same values in a property that irrefutably identifies the record as a duplicate entry is an L1 duplicate.
There’s hardly any AI that can be used here since the logic is standardized and structured.
This is why master data enrichment is generally a pre-cursors to master data de-duplication since enrichment allows several values to be updated into the system, that can then be leveraged for L1 de-duplication.
L2 duplicates, on the other hand, are far more complex and they’re generally used where the values required for L1 logic to be applied are simply missing.
AI has simply not evolved enough to fully automate L2 duplicate detection.
With that said, AI can make the job far more simpler, club likely duplicates by scanning the entire data record and the dataset itself before assigning a “duplicate confidence score”, that can then be assigned as a task to a human reviewer to either “accept” or “reject” the data records as duplicates.
After the Data Steward “accepts” the data records are generally merged, wherein the data from the 2nd record is deleted after the fields are created in the 1st record.
Below is a video demonstarting how our AI agent deduplicates the data:
Master Data Integrations for Multi-Domain Success
One of the more well-known critiques of a master data system is the fact that each master dataset, in practice, exists in isolation from the others in disparate organization systems.
To truly understand the performance of any given organizational function, interpreting data across master data domains is crucial.
Example 1: It’s important to know how many customers can be reached through any given marketing communication channel but it’s also important to know which products do these consumers favour the most, requiring deep integrations with product master data
Example 2: It’s important to know how many spare parts are in the procurement platform for provisioning for production activities but it’s also more important to know which of these parts required for upkeep of critical assets, these will be the critical spare parts.
This is only possible with deep integrations of asset master data with spare parts material master.
Data Governance
Master Data Governance frameworks aim to ensure that master data is accurate, secure, standardized, and compliant with internal policies and external regulations.
However, as data volumes grow and become more complex across distributed systems (ERP, PLM, CRM, SCM, etc.), manual enforcement of governance policies becomes unscalable and reactive.
Machine Learning brings proactive, intelligent automation into core data governance functions by enabling dynamic policy enforcement, anomaly detection, and intelligent decision support.
These models can continuously monitor and improve data quality while reducing the manual burden on data stewards.
Applications:
1. Smart Policy Checks
AI learns what clean, approved records usually look like and checks new entries for missing or incorrect fields, even if there’s no set rule.
Example: If most electric motor records have a “Voltage” field filled, and a new record doesn’t, AI flags it right away.
The AI looks at past records, learns which fields go together, and uses that knowledge to catch mistakes.
2. Spotting Anomalies and Errors
ML models detect unusual values or combinations in your data that don’t fit normal patterns.
Example: A “PVC pipe” listed with “1000 PSI” is flagged, because PVC typically can’t handle that much pressure.
AI builds a sense of what’s normal for each item type and catches outliers using pattern recognition models like Isolation Forest or Autoencoders.
3. Automated Record Approvals
AI scores new or updated records based on how clean and complete they are. High-confidence records can be auto-approved, while lower-confidence ones are sent to a human for review.
Example: An item classified as a “Hex Bolt,” with all fields correctly filled and matching past data, is approved automatically.
ML models calculate a confidence score based on how closely the record matches existing standards.
4. Helping Data Stewards in Real Time
AI supports data stewards while they’re working by suggesting values, pointing out missing fields, or flagging possible issues.
Example: While reviewing a material record, a steward sees AI suggestions for missing fields and gets alerts if something doesn’t match similar entries.
NLP and ML models run in the background and show intelligent hints and warnings directly in the interface.
Below are some use cases:
Customer Master Data:
In large multinational enterprises, new customer records often arrive via multiple channels, CRM portals, partner submissions, or internal uploads.
Missing email addresses, tax IDs, or incomplete billing information can delay invoicing and create compliance risks. AI governance systems automatically detect missing mandatory fields, enrich them using verified internal and external sources, and flag likely duplicates at the point of entry.
For example, if two records show “Acme Corp” and “Acme Corporation,” the system flags a potential duplicate and prevents redundant entries. This ensures high-quality customer data while reducing manual effort.
Materials and Spare Parts Data:
In the Materials or MRO domain, missing specifications, manufacturer part numbers, or unit-of-measure information is common, especially in legacy datasets or third-party imports.
These gaps hinder procurement, maintenance planning, and analytics workflows. AI-driven enrichment can infer missing values from existing records, cross-reference supplier catalogs, and standardize naming conventions across multiple plants.
For instance, a new record “SKF Bearing 6205” missing its outer diameter can be automatically enriched with the correct value from similar items in the database.
Likewise, a record listed as “GSKT, 4BOLT, SS316” can be enriched to specify a stainless steel gasket, identify flange type, and suggest compatible alternates.
Predictive models, such as regression algorithms or decision trees, can also estimate numeric fields like voltage, torque, or pressure ratings when explicit values are absent.
By identifying anomalies, harmonizing part numbers, standardizing descriptions, and suggesting alternates, AI ensures that the materials master becomes a reliable, actionable source of truth.
Below is our Verdantis’ data governance solution video:
Designing and Deploying AI in MDM
While the benefits of AI/ML in MDM are clear, successful adoption requires thoughtful integration with enterprise architecture and governance models:
Training Data Quality: The performance of AI/ML models is directly tied to the quality and representativeness of the historical data used to train them.
Domain-Specific Context: Off-the-shelf models often need tuning or retraining to handle domain-specific nuances in engineering, manufacturing, or procurement data.
Explainability and Trust: Users must be able to trace and understand how AI arrived at a particular decision or suggestion, especially in regulated industries.
Human-in-the-Loop (HITL): AI systems should be designed to augment and not replace the data stewards, allowing human oversight where needed and creating feedback loops for continuous improvement.
Conclusion
AI and ML are not just enhancing Master Data Management, but they are redefining what is possible. These technologies bring a level of speed, adaptability, and intelligence that manual and rule-based systems cannot match.
For MDM, MRO data management, and data governance, AI is no longer an emerging concept – it is a necessary capability for scaling data quality, accelerating decision-making, and future-proofing enterprise operations.
As organizations continue to embrace digital transformation, those that embed AI-driven intelligence into their master data practices will be better positioned to operate with agility, precision, and insight.
